30 citations · 31 across the 10 of their papers we have counts for
8 papers · 1 filter
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning
Renye Yan, Yaozhong Gan, You Wu +4
In sparse reward scenarios of reinforcement learning (RL), the memory mechanism provides promising shortcuts to policy optimization by reflecting on past experiences like humans. H…
The Exploration-Exploitation Dilemma Revisited: An Entropy Perspective
Renye Yan, Yaozhong Gan, You Wu +4
The imbalance of exploration and exploitation has long been a significant challenge in reinforcement learning. In policy optimization, excessive reliance on exploration reduces lea…
Transductive Off-policy Proximal Policy Optimization
Yaozhong Gan, Renye Yan, Xiaoyang Tan +2
Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature…
Reflective Policy Optimization
Yaozhong Gan, Renye Yan, Zhe Wu +1
On-policy reinforcement learning methods, like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), often demand extensive data per update, leading to sa…
Robust Action Gap Increasing with Clipped Advantage Learning
Zhe Zhang, Yaozhong Gan, Xiaoyang Tan
Advantage Learning (AL) seeks to increase the action gap between the optimal action and its competitors, so as to improve the robustness to estimation errors. However, the method b…
Smoothing Advantage Learning
Yaozhong Gan, Zhe Zhang, Xiaoyang Tan
Advantage learning (AL) aims to improve the robustness of value-based reinforcement learning against estimation errors with action-gap-based regularization. Unfortunately, the meth…
Stabilizing Q Learning Via Soft Mellowmax Operator
Yaozhong Gan, Zhe Zhang, Xiaoyang Tan
Learning complicated value functions in high dimensional state space by function approximation is a challenging task, partially due to that the max-operator used in temporal differ…
Trust Region-Guided Proximal Policy Optimization
Yuhui Wang, Hao He, Xiaoyang Tan +1
Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging ta…